Ab initio structure prediction methods have been nowadays widely used as powerful tools for structure searches and materials discovery. However, they are generally restricted to small systems owing to the heavy computational cost of the underlying density functional theory (DFT) calculations in structure optimizations. In this work, by combining a state-of-art machine learning (ML) potential with our in-house developed CALYPSO structure prediction method, we developed two acceleration schemes for the structure prediction of large systems, in which a ML potential is pre-constructed to fully replace DFT calculations or trained in an on-the-fly manner from scratch during the structure searches. The developed schemes have been applied to medium- and large-sized boron clusters, both of which are challenging cases for either the construction of ML potentials or extensive structure searches. Experimental structures of B₃₆ and B₄₀ clusters can be readily reproduced, and the putative global minimum structure for the B₈₄ cluster is proposed, where the computational cost is substantially reduced by ∼1–2 orders of magnitude if compared with full DFT-based structure searches. Our results demonstrate a viable route for structure prediction in large systems via the combination of state-of-art structure prediction methods and ML techniques.

中文翻译：

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通过数据驱动的势能面学习来加速CALYPSO结构预测

从头算如今，结构预测方法已广泛用作结构搜索和材料发现的强大工具。但是，由于结构优化中底层密度泛函理论（DFT）计算的巨大计算成本，它们通常限于小型系统。在这项工作中，通过将最新的机器学习（ML）潜能与我们内部开发的CALYPSO结构预测方法相结合，我们针对大型系统的结构预测开发了两种加速方案，其中ML潜能构造为完全替代DFT计算或在结构搜索过程中从头开始进行实时培训。已开发的方案已应用于中型和大型硼团簇，无论是建立ML潜力还是进行广泛的结构搜索，这都是具有挑战性的案例。B的实验结构可以很容易地复制₃₆和B ₄₀群集，并提出了B ₈₄群集的推定全局最小结构，与基于DFT的完整结构搜索相比，计算成本可以大幅度降低1-2个数量级。我们的结果证明了通过结合最新的结构预测方法和ML技术在大型系统中进行结构预测的可行途径。